Adaptive Learning Environments 1

This web page now refers to the 2013-2014 version of the course which will run from January to March 2014. This course was previously a level 10/11 course but is now designated as level 11. Click here to see the old course pages.

Lecturer: Alyssa Alcorn
Informatics Forum 5.32
Contact e-mail: aalcorn (at) ed (dot) ac (dot) uk
Note that I will not be supervising any MSc projects this year, nor honours projects for 2014-2015. This is not negotiable. Please do not ask!

Class dates and times

Tuesdays and Fridays from 12:10-13:00, beginning on Tuesday January 14th.
The class meets at Minto House, LR1.
Lecturer Office Hour: Tuesdays from 13:00-14:00 in Appleton Tower, room 5.02. There is no office hour during Innovative Learning Week (February 18th).

Links (to be completed)

Office hour and e-mail policy (PLEASE READ ASAP!)
Course schedule, including information on deadlines and links to class materials
Big questions and high-level themes for the course
Reading list (divided by lectures) indicating REQUIRED and SUGGESTED materials
Unit 1: Information and materials for Student seminar series 1 (SSS1) and Assignment 1, including SSS1 papers
Unit 2: Information and materials for Student seminar series 2 (SSS2) and Assignment 2, including SSS2 topics
Feedback and marking information, including marking guides (rubrics) for the assignments
Exam information and past papers-- to be updated
General resources --Last updated Jan. 24

New links--Try some ALEs!

Cognitive Tutor 90-day trial available free from Carnegie Learning. Click on "Try a demo now" (left side).
Try Betty's Brain for free. Register at Vanderbilt Teachable Agents website under Try Betty's Brain. NB: Need internet access continuously while using.
Try ANDES physics tutor free from the ANDES project website. Remember that this is supposed to be homework help for a more traditional course, and probably can't teach you physics all by itself.

Course overview: How is ALE organised?

This course consists of a mixture of lectures, small-group class activities, student-led seminars, independent readings, and full-class discussions. There is an additional (attendance-optional) office hour after class on Tuesdays, where the lecturer is available to continue class discussions or provide additional help.

Core Systems:
The course begins by presenting introductory lectures and readings about several core systems (i.e. existent adaptive learning environments) that will be referenced throughout the semester as examples for more specific topic units, including but not limited to user modelling, metacognition, feedback, and system evaluation. Topic units will be supported by additional reading and class activities. Currently, there is one scheduled guest lecture related to educational data mining.

Big Questions:
The course also explicitly asks four "big questions" about what adaptive learning environments ARE, and what they have tried to accomplish (and how/why). Throughout the course, the class will collaborate to gather evidence related to these questions from the various systems, topics, and papers discussed, and will try to collectively generate some answers. The course will culminate with a class debate about whether or not we believe that ALE/ITS research has (so far) succeeded in accomplishing what we have identified as its main goals.

Student Seminar Series (SSS)
The student seminar series (SSS) combine out-of-class preparation with a student-led presentation to the rest of the class. The seminar tasks are designed to add more in-depth information to the introductory lectures, and allow students to practice key skills for the assignments in a context where their classmates can help them, and where they can get formative feedback (see below). There are two seminar series, with all students participating in each series as a part of a small group (about 4 students).

Assignments:
Each seminar series is linked to a written assignment. This assignment will build directly on the task that students have accomplished in their seminar, and the seminar readings. Thus, the seminar instructions and the assignment are introduced at the same time, so that students have a chance to choose the combined seminar/assignment topic that interests them most.

Feedback and marking:
For each student seminar series (SSS), each of the groups will receive formative feedback on how well they have accomplished their task, with specific comments for improvement before they submit the assignment. Each student's essay assignments will receive summative feedback in the form of a completed marking guide (i.e. rubric) assessing the work on various categories/subtasks. Blank marking guides (rubrics) will be made available to students ahead of time so that they can check their own work against the criteria. Each student will also receive feedback on points where s/he is already doing well, and priorities for improving on the next assignment (or on the essay question for the exam, in the case of assignment 2). As late feedback was a problem the previous time this course was taught, we have recruited an additional marker to ensure that feedback can be returned promptly. This year's marker is Clare Llewellyn. Please DO NOT contact her with general course questions or clarification on the assignment requirements; these queries should go to the lecturer, Alyssa Alcorn.

The Exam (information is provisional and will be confirmed in January)
The exam is expected to consist of 4-5 short-answer questions (several paragraphs each) and a choice between several longer essay questions. The short answer questions may ask about specific concepts and about the core systems. The essay questions will be similar to the assignments in that they ask you to give explanations or suggest solutions to problems based on evidence from the field-- except that unlike the assignments you can draw on ANY evidence from any system(s), papers, and studies, as long as this evidence is relevant, detailed, and accurate. Diligently memorising isolated facts about systems will not be a very good strategy for the long essays, as you will see if you look at previous years' exams. Thinking in terms of argumentation, evidence, and the course's "big questions" is likely to be a much better strategy (and also a far more interesting one).

FAQ 1: How much programming is in this course?
There is ZERO programming in this course. We will be talking about existing adaptive learning environments and about new designs at a fairly high level, not at a detailed implementation level. That said, knowledge of programming will certainly add to your understanding of the course materials.

FAQ 2: Is this course suitable for visiting students? Yes. Visiting students are very welcome. Anyone with some general background in any of the following subjects should be fine to take this course: cognitive science, education, psychology, human-computer interaction, dialogue systems, AI. Unlike some other courses in Informatics, ALE-1 does not build directly on very specific content from earlier courses.


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